libraries | multiple languages for working with the CoinSpark protocol | Blockchain library

I fixed my issue.

Here are the steps of solving that issue.

1 ) Download CUDA from https://developer.nvidia.com/cuda-downloads

2 ) Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download

3 ) Copy bin,include and lastly lib from CUDNN zip file and paste it C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA{version}

4 ) Then run the .py code in PyCharm and it perceives GPU at last.

This can be done in C# by customizing the JsonMediaTypeFormatter (from the NuGet package Microsoft.AspNet.WebApi.Client) like so:

I was able to get it resolved, but the solution is...hacky.

So, apparently, Google Apps Script exports only what is in globalThis of a project: just the functions and variables. No classes, no constants, ...

Probably has a lot to do with how ES6 works, with its globalThis behavior. One can see that in action, by creating a dummy function, a dummy variable, and a dummy class in their local developer console:

Make Sure "http://" is in your URL Address .

1. change from localhost to your ip
2. add http://

http://192.168.43.49:3000/user/

I faced same issue, it happens in Android, but works well in IOS. I guess this issue about Flipper Network.

For while, I commented

initializeFlipper(this, getReactNativeHost().getReactInstanceManager())

in this file /android/app/src/main/java/com/{your_project}/MainApplication.java

Whoever is still struggling with this issue. it's happening because of Flipper network plugin. I disabled it and things work just fine.

My workaround to make this work is commenting out line number 43

I answer by myself referencing this GitHub issue: https://github.com/psf/requests/issues/4775

I solved the problem using the code below:

No. It is fine to just have it in val.

The issue with fragments is that Fragment and its view have different lifecycles, so holding onto the binding in fragment between onDestroyView() and onDestroy() uses unnecessary memory.

In custom views, it has same lifecycle as any binding stored in a class property.

You can use regular expressions (Regex) to achieve that. You’ll essentially need to import the library and then provide a pattern to have the command recognize the letters from the numbers. As an example:

Inside your requirements.txt change scipy line with this scipy==1.6.0 and save. Now retry pip installation.

I think when you are using the conda environment. The conda list is going to show all the general packages that shared by the same conda environment. And the reason why 'tensorflow-gpu' is listed in 'pip freeze', but not in 'conda list', is because you used pip install to installed 'tensorflow-gpu'(could be you or the IDE). In this case, 'tensorflow-gpu' is only exists under this python project I believe. Actually, there is an official document about this topic.

Issues may arise when using pip and conda together. When combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip. When appropriate, conda and pip requirements should be stored in text files.

Use pip only after conda Install as many requirements as possible with conda then use pip.

Pip should be run with --upgrade-strategy only-if-needed (the default).

Do not use pip with the --user argument, avoid all users installs.

And here is the link.